A Neural Combinatorial Optimization Algorithm for Unit Commitment in AC Power Systems
Bibliographic record
Abstract
The unit commitment (UC) problem in AC power systems can be formulated as a mixed-integer nonlinear optimization program with a running time that scales exponentially with the number of generators. This paper addresses the time complexity of solving the UC problem by developing a deep learning framework that determines the generator on/off states using a transformer deep neural network (DNN), and subsequently solves an AC optimal power flow (OPF) problem to obtain the generator setpoints. To obtain a feasible binary solution, we apply a neural combinatorial optimization algorithm to train the DNN, while penalizing infeasible power flow solutions. Also, to guarantee the optimality of the generator setpoints, we transform the AC OPF problem into a semidefinite program (SDP). The proposed algorithm can obtain a near-optimal solution to the UC problem in polynomial running time. Simulations are performed for two IEEE test systems. When compared with three existing UC algorithms in the literature, our proposed algorithm can obtain a solution with at least 2.14% lower operation cost and lower running time. When compared with the MOSEK solver, our algorithm can obtain a solution with at most 1.97% greater operation cost, but with a significantly lower running time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".